Detectors based on monolithic scintillation crystals, for example monolithic scintillation detectors for Positron Emission Tomography (PET) or Single Photon Emission Computed Tomography (SPECT) imaging, have many advantages over pixelated detectors. Replacing pixelated detectors by monolithic scintillation crystal detectors may reduce the cost per unit volume and may increase the sensitivity and the spatial, energy and timing resolution of the detector. In addition, on thick monolithic crystal-based detectors, the depth-of-interaction (DOI) may be determined without additional hardware. However, monolithic crystal-based detectors are known to perform poorly if a conventional, simple method known in the art is used for obtaining spatial information of interaction events. Such a conventional method could for example comprise a centroid calculation, e.g. Anger logic, followed by a distortion correction.
Improved performance has been obtained by introducing different techniques, e.g. maximum likelihood, artificial neural networks or k-nearest neighbours, which make use of training data characterized by known interaction locations. Information derived from these training data may then be used for the positioning of new events. However, the acquisition of training data may be a complex and time-consuming process, may happen off-line and may require an accurate robotic stage. These drawbacks entail the main limitation for practical use of monolithic crystals in commercial systems.
In order to achieve a good detector performance, monolithic detectors may require costly and complex calibration procedures, which may have slowed the introduction of monolithic crystal-based detectors in current commercial systems. The calibration procedures known in the art often require training data, and thus require a long and complex procedure for obtaining these training data. For example, such methods may require data acquisition over many hours or even days in order to calibrate one detector. If several detectors need to be calibrated in parallel, this entire setup has to be replicated as many times. In addition, calibration procedures known in the art cannot be performed on detectors which are already assembled in the gantry of the imaging scanner, since this may not allow a proper follow-up of the calibration status.
Therefore, there exists a need for a simple and efficient calibration method, such that monolithic crystal detectors may become feasible in commercial scanners. Some efforts have been made to simplify the acquisition of training data. However, the training process may still require individual detector calibration for non-assembled detectors, which does not allow for detector recalibration once the detectors are assembled in the system and the system is operational in the field. This also constitutes an important drawback in modern systems that use avalanche photo diode (APD) detectors or silicon photomultiplier (SiPM) photosensors for the readout of the scintillation crystals. The performance of those devices (gain, dark current, photon detection efficiency (PDE) . . . ) is very sensitive to temperature and supplied voltage variations, requiring for flexible calibration procedures under different working conditions. One such method may be disclosed by Dam et al., in “Improved Nearest Neighbor Methods for Gamma Photon Interaction Position Determination in Monolithic Scintillator PET Detectors”, IEEE Transactions on Nuclear Science, 58(5), pages 2139 to 2147.
Other known positioning methods, which are not based on training data may have been disclosed, but these may be based on parameterized models of the detector response. Such methods rely on accurate knowledge of the detector response and the variable performance of the detector. For example, methods may be known in the art which use parametric modelling, such that no training data are needed. Such parametric modelling calibration methods may rely on assumptions made on detector behaviour. However, various factors which are difficult to predict in an analytical approach may influence the behaviour of a detector significantly, such as assembling differences, photosensor gain variations, electronic noise, or couplings. Therefore, a method for calibration that takes measurable data for each specific detector into account is desired. For example, one such parametric method may be disclosed by Zhi et al., in “Nonlinear least-squares modeling of 3D interaction position in a monolithic scintillator block,” Phys. Med. Biol. 55 (2010) 6515-6532.